972 citations · 1.2k across the 30 of their papers we have counts for
30 papers
Perception, Reason, Think, and Plan: A Survey on Large Multimodal Reasoning Models
Yunxin Li, Zhenyu Liu, Zitao Li +19
Reasoning lies at the heart of intelligence, shaping the ability to make decisions, draw conclusions, and generalize across domains. In artificial intelligence, as systems increasi…
VideoVista: A Versatile Benchmark for Video Understanding and Reasoning
Yunxin Li, Xinyu Chen, Baotian Hu +3
Despite significant breakthroughs in video analysis driven by the rapid development of large multimodal models (LMMs), there remains a lack of a versatile evaluation benchmark to c…
Uni-MoE: Scaling Unified Multimodal LLMs with Mixture of Experts
Yunxin Li, Shenyuan Jiang, Baotian Hu +5
Recent advancements in Multimodal Large Language Models (MLLMs) underscore the significance of scalable models and data to boost performance, yet this often incurs substantial comp…
SelectIT: Selective Instruction Tuning for LLMs via Uncertainty-Aware Self-Reflection
Liangxin Liu, Xuebo Liu, Derek F. Wong +4
Instruction tuning (IT) is crucial to tailoring large language models (LLMs) towards human-centric interactions. Recent advancements have shown that the careful selection of a smal…
Towards Faithful Explanations for Text Classification with Robustness Improvement and Explanation Guided Training
Dongfang Li, Baotian Hu, Qingcai Chen +1
Feature attribution methods highlight the important input tokens as explanations to model predictions, which have been widely applied to deep neural networks towards trustworthy AI…
Towards Reasoning in Large Language Models via Multi-Agent Peer Review Collaboration
Zhenran Xu, Senbao Shi, Baotian Hu +4
Large Language Models (LLMs) have shown remarkable capabilities in general natural language processing tasks but often fall short in complex reasoning tasks. Recent studies have ex…
A Survey of Large Language Models Attribution
Dongfang Li, Zetian Sun, Xinshuo Hu +5
Open-domain generative systems have gained significant attention in the field of conversational AI (e.g., generative search engines). This paper presents a comprehensive review of…
Temporal Knowledge Question Answering via Abstract Reasoning Induction
Ziyang Chen, Dongfang Li, Xiang Zhao +2
In this study, we address the challenge of enhancing temporal knowledge reasoning in Large Language Models (LLMs). LLMs often struggle with this task, leading to the generation of…
A Comprehensive Evaluation of GPT-4V on Knowledge-Intensive Visual Question Answering
Yunxin Li, Longyue Wang, Baotian Hu +5
The emergence of multimodal large models (MLMs) has significantly advanced the field of visual understanding, offering remarkable capabilities in the realm of visual question answe…
ExplainCPE: A Free-text Explanation Benchmark of Chinese Pharmacist Examination
Dongfang Li, Jindi Yu, Baotian Hu +2
As ChatGPT and GPT-4 spearhead the development of Large Language Models (LLMs), more researchers are investigating their performance across various tasks. But more research needs t…
LMEye: An Interactive Perception Network for Large Language Models
Yunxin Li, Baotian Hu, Xinyu Chen +3
Training a Multimodal Large Language Model (MLLM) from scratch, like GPT-4, is resource-intensive. Regarding Large Language Models (LLMs) as the core processor for multimodal infor…
Enhancing Multi-modal and Multi-hop Question Answering via Structured Knowledge and Unified Retrieval-Generation
Qian Yang, Qian Chen, Wen Wang +2
Multi-modal multi-hop question answering involves answering a question by reasoning over multiple input sources from different modalities. Existing methods often retrieve evidences…
Calibration Meets Explanation: A Simple and Effective Approach for Model Confidence Estimates
Dongfang Li, Baotian Hu, Qingcai Chen
Calibration strengthens the trustworthiness of black-box models by producing better accurate confidence estimates on given examples. However, little is known about if model explana…
Prompt-based Text Entailment for Low-Resource Named Entity Recognition
Dongfang Li, Baotian Hu, Qingcai Chen
Pre-trained Language Models (PLMs) have been applied in NLP tasks and achieve promising results. Nevertheless, the fine-tuning procedure needs labeled data of the target domain, ma…
An Efficient Memory-Augmented Transformer for Knowledge-Intensive NLP Tasks
Yuxiang Wu, Yu Zhao, Baotian Hu +3
Access to external knowledge is essential for many natural language processing tasks, such as question answering and dialogue. Existing methods often rely on a parametric model tha…
Hansel: A Chinese Few-Shot and Zero-Shot Entity Linking Benchmark
Zhenran Xu, Zifei Shan, Yuxin Li +2
Modern Entity Linking (EL) systems entrench a popularity bias, yet there is no dataset focusing on tail and emerging entities in languages other than English. We present Hansel, a…
Chunk-aware Alignment and Lexical Constraint for Visual Entailment with Natural Language Explanations
Qian Yang, Yunxin Li, Baotian Hu +3
Visual Entailment with natural language explanations aims to infer the relationship between a text-image pair and generate a sentence to explain the decision-making process. Previo…
GlyphCRM: Bidirectional Encoder Representation for Chinese Character with its Glyph
Yunxin Li, Yu Zhao, Baotian Hu +5
Previous works indicate that the glyph of Chinese characters contains rich semantic information and has the potential to enhance the representation of Chinese characters. The typic…
Multi-hop Graph Convolutional Network with High-order Chebyshev Approximation for Text Reasoning
Shuoran Jiang, Qingcai Chen, Xin Liu +2
Graph convolutional network (GCN) has become popular in various natural language processing (NLP) tasks with its superiority in long-term and non-consecutive word interactions. How…
You Can Do Better! If You Elaborate the Reason When Making Prediction
Dongfang Li, Jingcong Tao, Qingcai Chen +1
Neural predictive models have achieved remarkable performance improvements in various natural language processing tasks. However, most neural predictive models suffer from the lack…
Neural Data-to-Text Generation with Dynamic Content Planning
Kai Chen, Fayuan Li, Baotian Hu +3
Neural data-to-text generation models have achieved significant advancement in recent years. However, these models have two shortcomings: the generated texts tend to miss some vita…
Decomposing Word Embedding with the Capsule Network
Xin Liu, Qingcai Chen, Yan Liu +4
Word sense disambiguation tries to learn the appropriate sense of an ambiguous word in a given context. The existing pre-trained language methods and the methods based on multi-emb…
Text-Guided Neural Image Inpainting
Lisai Zhang, Qingcai Chen, Baotian Hu +1
Image inpainting task requires filling the corrupted image with contents coherent with the context. This research field has achieved promising progress by using neural image inpain…
Sentence Simplification with Memory-Augmented Neural Networks
Tu Vu, Baotian Hu, Tsendsuren Munkhdalai +1
Sentence simplification aims to simplify the content and structure of complex sentences, and thus make them easier to interpret for human readers, and easier to process for downstr…